Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification

@article{Qaiser2021MultipleIL,
  title={Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification},
  author={Talha Qaiser and Stefan Winzeck and Theodore Barfoot and Tara D Barwick and Simon J. Doran and Martin F. Kaiser and Linda Wedlake and Nina Tunariu and Dow-Mu Koh and Christina Messiou and Andrea G. Rockall and Ben Glocker},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.07805}
}
Whole body magnetic resonance imaging (WB-MRI) is the recommended modality for diagnosis of multiple myeloma (MM). WBMRI is used to detect sites of disease across the entire skeletal system, but it requires significant expertise and is time-consuming to report due to the great number of images. To aid radiological reading, we propose an auxiliary task-based multiple instance learning approach (ATMIL) for MM classification with the ability to localize sites of disease. This approach is appealing… 

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